Showing papers on "Probabilistic latent semantic analysis published in 1993"

Latent variable modeling of growth with missing data and multilevel data

Abstract: Latent variable modeling in psychometrics is connected with mainstream statistical theory in the areas of random coefficients, missing data, and clustered data. An educational achievement example points to the need for integrating these developments in a single analysis framework. It is shown that existing methods and software for latent variable modeling accomplish this.

A latent class vector model for preference ratings

Abstract: A latent class formulation of the well-known vector model for preference data is presented. Assuming preference ratings as input data, the model simultaneously clusters the subjects into a small number of homogeneous groups (or latent classes) and constructs a joint geometric representation of the choice objects and the latent classes according to a vector model. The distributional assumptions on which the latent class approach is based are analogous to the distributional assumptions that are consistent with the common practice of fitting the vector model to preference data by least squares methods. An EM algorithm for fitting the latent class vector model is described as well as a procedure for selecting the appropriate number of classes and the appropriate number of dimensions. Some illustrative applications of the latent class vector model are presented and some possible extensions are discussed.

Latent Structure Models for Ranking Data

Abstract: In this paper several latent structure models for analyzing data that consist of complete or incomplete rankings are discussed. First, attention is given to some latent class extensions of the Bradley-Terry-Luce model for ranking data. Next, various latent class models based on log-linear modeling of ranking data are described. Within this latter family of latent class models, a main distinction is made between models based on the assumption of quasi-independence within the latent classes, and models in which some form of association between the ranking positions is allowed to exist within the classes. All models are applied to a real data set from a large scale cross-national survey on political values.

Search algorithms for numeric and quantitative data

Abstract: Search algorithms underpin astronomical databases, and may be called upon for the processing of (suitably coded) textual data. They may be required in conjunction with the use of dimensionality reduction approaches such as the factor space approach described in chapter 3, or latent semantic indexing (Deerwester et al., 1990). Efficient search algorithms can be the building blocks of data reorganization approaches using clustering (see section 4.8 below). All in all, search algorithms constitute the motor which drives information retrieval.

Fixed-distance latent class models for the analysis of sets of two-way contingency tables.

Abstract: This paper develops and describes the application of modified latent class models for analyzing sets of two-way contingency tables. The proposed fixed-distance models differ from traditional latent class models in that the assumption of local stochastic independence is superseded by allowing interactions of the manifest variables within each class, which can be represented by a single association parameter. As an example, two data sets on eye color by hair color [collected in Caithness (N1 = 5,387) and Aberdeen (N2 = 22,361)] and fixed-distance models with up to six classes (three classes per data set) are considered, finally leading to satisfactory fit and rather simple interpretation.